A multivariate blood metabolite algorithm stably predicts risk and resilience to major depressive disorder in the general population.

ABSTRACT:

Background

Socioeconomic pressures, sex, and physical health status strongly influence the development of major depressive disorder (MDD) and mask other contributing factors in small cohorts. Resilient individuals overcome adversity without the onset of psychological symptoms, but resilience, as for susceptibility, has a complex and multifaceted molecular basis. The scale and depth of the UK Biobank affords an opportunity to identify resilience biomarkers in rigorously matched, at-risk individuals. Here, we evaluated whether blood metabolites could prospectively classify and indicate a biological basis for susceptibility or resilience to MDD.

Methods

Using the UK Biobank, we employed random forests, a supervised, interpretable machine learning statistical method to determine

SUBMITTER: Radford-Smith DE 

PROVIDER: S-EPMC10275706 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature